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Author:

Xu, Hao (Xu, Hao.) | Pei, Fujun (Pei, Fujun.) | Jiang, Ning (Jiang, Ning.)

Indexed by:

CPCI-S

Abstract:

In this paper, an in-motion initial alignment algorithm based on Lie group is proposed, which effectively solves the non-uniqueness of the unit-quaternion attitude description and the nonlinear problem when using unit-quaternion for state estimation. The algorithm divides the attitude matrix into three successive special orthogonal matrices according to the property of the rotation on Lie group description and the basic principle of inertial navigation, and isolates the angular velocity and linear velocity of the carrier under dynamic conditions. Based on the mapping relation between Lie group and Lie algebra and the optimal estimation principle, and the uncertainty of measurement noise in modeling is analyzed, an adaptive Lie group filter method is proposed to estimate the initial inertial matrix directly. It can be seen from the experimental results that compare with the unit quaternion algorithm proposed algorithm has improved the alignment accuracy and stability. This method is an excellent situation to the SINS initial alignment in-motion.

Keyword:

Lie group filter innovation adaptive strapdown inertial navigation initial alignment

Author Community:

  • [ 1 ] [Xu, Hao]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Pei, Fujun]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Jiang, Ning]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Xu, Hao]Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
  • [ 5 ] [Pei, Fujun]Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
  • [ 6 ] [Jiang, Ning]Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China

Reprint Author's Address:

  • [Xu, Hao]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China;;[Xu, Hao]Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China

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Source :

2018 37TH CHINESE CONTROL CONFERENCE (CCC)

ISSN: 2161-2927

Year: 2018

Page: 4581-4586

Language: English

Cited Count:

WoS CC Cited Count: 2

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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